Computational_Practicum_Lecture_Coding

Welcome to the Lecture Examples repository! This repository contains Python code files used as examples during my lectures. Each lecture is organized into a separate folder for easy navigation and reference.

Table of Contents

1. Q1 Lecture 1: Introduction to course, basic programming
2. Q1 Lecture 2: Linear algebra, eigenvectors, linear systems
3. Q1 Lecture 3: Nonlinear equation systems
4. Q1 Lecture 4: Integration, errors, differentiation
5. Q1 Lecture 5: Ordinary differential equations, initial value problems, forward Euler, backward Euler
6. Q1 Lecture 6: Boundary value problem, finite difference method
7. Q1 Lecture 7: Boundary value problem, shooting method
8. Q2 Lecture 1: Advanced programming
9. Q2 Lecture 2: Partial differential equations, finite difference method
10. Q2 Lecture 3: Partial differential equations in multiple space dimensions and time
11. Q2 Lecture 4: Optimization 1
12. Q2 Lecture 5: Optimization 2
13. Q2 Lecture 6: Regression, interpolation, least squares

Folder Structure

Each lecture is organized into a separate folder with a meaningful name to help you quickly find the relevant code examples. Inside each folder, you'll find Python code files (.ipynb) and any additional resources or documentation related to the lecture.

lecture_1/
├── example1.ipynb
├── example2.ipynb
├── ...
├── README.md (Optional: Additional information about Lecture 1)

Run code online

You can also run the code online via Binder or Google's Colaboratory. Binder is a free, open-source web service that packages Jupyter notebooks inside an executable container, which can be run within a web browser, no installation required. Colab allows users with Google accounts to execute Jupyter notebooks on the Google cloud.

To execute the notebook in Binder:

1. Click the launch binder button above. Once the demo launches, click My_sample_notebook.ipynb in the file listing.
2. Run the notebook by selecting Cell > Run All.

To execute the notebook in Colab:

1. Click the Open in Colab button above. It will launch the notebook directly.
2. Navigate to the lecture folder you are interested in and open the notebook you are interested in.
3. Make the notebook live by clicking 'Connect' in the Colab toolbar.
4. Select Runtime > Run All in the menu to execute the notebook. (You may get a warning that the page was not authored by Google.)

Getting Started and run code locally

1. Clone this repository to your local machine:

   git clone https://github.com/your-username/lecture-examples.git
   
   

2. Navigate to the specific lecture folder you are interested in.

3. Explore the Python code files provided as examples during the lecture.

Usage

Feel free to use these code examples for reference or in your own learning journey. If you have any questions or need further explanations, please don't hesitate to reach out.

Copyright and license

Copyright (C) 2023 Artur Schweidtmann and Zoe Gromotka TU Delft

This repository is open-source and available under the MIT License. Feel free to use, modify, and distribute the code examples as needed.

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